Perpendicular Vegetation Index Research Articles

A novel ecological evaluation index based on geospatial principles and remote sensing techniques

ABSTRACT The evaluation of regional ecological status has far-reaching significance for understanding regional ecological conditions and promoting sustainable development. Herein, a geospatial ecological index (GEI) was developed on the basis of Landsat data and the principles of soil lines and spatial geometry. Specifically, the GEI integrates four remote sensing indicators: Perpendicular Vegetation Index (PVI) representing greenness, Modified Perpendicular Drought Index (MPDI) representing drought, Normalized Difference Built-up and Soil Index (NDSI) representing the dryness of land surface, and Land Surface Temperature (LST) representing the hotness of land surface. Two typical regions, Fuzhou City and Zijin mining area, in Fujian Province, China, were selected to evaluate regional ecological quality via the proposed GEI. The results show an improvement in the overall ecological quality of Fuzhou City, with an increase in the average GEI value from 0.49 in 2001 to 0.53 in 2020. In the case of the Zijin mining area, regions with poor ecological status are concentrated in the main mining areas. However, the average GEI value rose from 0.51 in 1992 to 0.57 in 2020, illustrating an improvement in its ecological conditions. The study demonstrates the robustness and effectiveness of GEI, objectively revealing the spatial distribution and ecological status.

Estimating the spatial distribution of soil organic carbon and soil total nitrogen concentrations by remote sensing methods: a case study in a forest-dominated Alpine Watershed, Western China

ABSTRACT In ecologically fragile areas, accurate estimation of soil organic carbon (SOC) and soil nitrogen (STN) concentrations is a prerequisite for sustainable development. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0–30 cm) into three soil layers of 0–10 cm, 10–20 cm, and 20–30 cm to carry out SOC and STN concentrations estimation experiments in the Qilian Mountains in western China. To estimate SOC and STN concentrations, a stepwise multiple linear regression model was used. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results show that the stepwise multiple linear regression model has stable simulation performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen content. The ranking of SOC concentrations in the 0–30 cm soil layer was bush woods > spruce forests > sabina forests > alpine meadows > steppes, with mean concentrations of 117.98 g/kg, 101.35 g/kg, 83.09 g/kg, 78.76 g/kg, and 37.08 g/kg, respectively. The ranking of STN concentrations in the 0–30 cm soil layer was bush woods > alpine meadows >spruce forests > sabina forests > steppes, with mean concentrations of 4.77 g/kg, 3.71 g/kg, 3.19 g/kg, 3.27 g/kg, and 1.92 g/kg, respectively. Both SOC and STN concentrations decreased significantly with increasing soil depth.

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.

Wheat leaf traits monitoring based on machine learning algorithms and high-resolution satellite imagery

The leaf, which is a crucial indicator for evaluating crop status, plays an important role in plants' functions. Determining and monitoring leaf parameters can facilitate the detection and estimation of crop yield, which is essential for food security. Crop monitoring by remote sensing technology is critical to support crop production, especially over large scales. In this study, we developed a methodology to estimate leaf parameters based entirely on vegetation indices (VIs) from remotely sensed imagery in wheat under different management practices. Therefore, the current study aimed to examine the utility of VIs calculated from the sentinel-2 data in estimating the Leaf area index (LAI) and leaf parameters at wheat farms using machine learning algorithms. Leaf parameters included leaf dry weight (LDW), specific leaf area (SLA) and leaf specific weight (SLW), and machine learning algorithms were SVM (support vector machine), ANN (artificial neural network) and DNN (deep neural network). Leaf parameters were measured at several developmental stages of wheat in two contrasting environments in the southern Iran. The results demonstrated that the DNN algorithm could efficiently predict leaf parameters in the southern Iran with an overall precision of >72%, which assessed the potential of employing DNN to achieve the temporal and spatial distribution data of wheat based on the Sentinel-2 imagery. The validation of the DNN model generally showed high accuracy (R = 0.80, RMSE = 1.19, and MAE = 0.98) between observed and estimated LAI values when this model was used. NDVI was also highly sensitive to wheat LDW and SLA parameters, with a good correlation between field measurements and those predicted by the DNN model from sentinel-2 imagery, with the R values of 0.66 and 0.85, respectively. Further, NDVI and PVI (Perpendicular Vegetation Index) were linearly correlated with SLW across both temporal and spatial scales (R = 0.79). Among VIs considered from sentinel-2 imagery to predict wheat leaf parameters, NDVI was more sensitive than other VIs. This research, thus, indicated that using sentinel-2 data within a DNN model could provide a comparatively precise and robust prediction of leaf parameters and yield valuable insights into crop management with high temporal and spatial accuracy.

An Investigation of the Forests of Pernik Province (Western Bulgaria) by the Use of the Perpendicular Vegetation Index (PVI)

The current research represents a pilot study for application of the Perpendicular Vegetation Index (PVI) for an area with forests in Bulgaria. It is the first of its kind when it comes to forest studying in the country to the best knowledge of the author. When it comes to soil background Landsat images and other spectral data may be used for monitoring forest territories as well. The study area is Pernik Province which is located in the western parts of Bulgaria. The main aim is to investigate the PVI for the forests of Pernik Province. The index has been calculated by the application of Landsat 8 bands. The PVI has been processed for several months of different years. The main focus is both on the beginning and the end of the growing season when there are significant changes in leaf biomass. The results are promising and show typical vegetation features in the beginning of the growing season (April), a well-developed vegetation (July) and a steadily decreasing biomass in November.

Comparing satellites and vegetation indices for cover crop biomass estimation

• Adequate biomass is required for cover crops to provide significant ecological benefits. • Satellite imagery predicted field level cover crop biomass accurately. • Satellite imagery more accurately estimated biomass than handheld sensors. • At least one vegetation index for each satellite system accurately predicted biomass. • NDVI was not the best predictor of cover crop biomass for any satellite. Cost-share programs based on measures of participation rather than performance are available to farmers who plant cover crops. However, cover crops only provide significant ecological benefits like reduced nutrient loss when adequate biomass is established. The purpose of this study was to determine whether satellite imagery can effectively estimate cover crop biomass in fields with diverse species composition, and whether increased spatial resolution and satellite imaging frequency can increase biomass estimation accuracy. Aboveground biomass samples of 1 m 2 were collected for 86 sites within 26 agricultural fields containing unique cover crop species composition to assess biomass production. In-field sensors were used to measure normalized difference vegetation index (NDVI) and groundcover percentage. Three satellites (Landsat-8 [30 m resolution], Sentinel-2 [10 m resolution], and PlanetScope [3 m resolution]) were used to calculate eight vegetation indices (VIs) for comparison with cover crop biomass. Multiple linear regression, correlation coefficients, and root mean square error (RMSE) were used to perform hierarchical clustering to rank VIs calculated from each satellite for biomass estimation accuracy. Satellites predicted cover crop biomass at the field level very accurately (r 2 up to 0.79), demonstrating the potential of large-scale biomass estimation at relatively low cost compared to in-field sampling. All satellite-VI pairs estimated biomass more accurately than the in-field sensors. Performance of VIs varied by satellite, but each satellite had at least one VI that performed very well for both site-level and field-averaged data. When using PlanetScope or Landsat-8 imagery, the perpendicular vegetation index provided the most accurate cover crop biomass estimation on a per-site basis and ratio vegetation index performed best using Sentinel-2 imagery. PlanetScope was the only satellite to provide useable imagery for every site due to increased revisit period; however, its increased spatial resolution did not increase estimation accuracy overall compared to Landsat-8 or Sentinel-2.

Daljinski indeksi vegetacije izračunati programom SAGA GIS - poređenje vertikalnog i prilagođenog pristupa promena zemljišta za sliku LANDSAT TM

Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

Remote Sensing Analysis of Agricultural Drone

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of thettest on vegetation indices showed that six out of ten vegetation indices were significant atp<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlationsr = 0.66, 0.65, 0.65, 0.63 and regression results were ofR2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height werer = 0.46, 0.43, 0.43, and 0.49 and its regressions were ofR2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

A Novel NIR–Red Spectral Domain Evapotranspiration Model From the Chinese GF-1 Satellite: Application to the Huailai Agricultural Region of China

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) of 0.77–0.84 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.01$ </tex-math></inline-formula> ) and root-mean-square error (RMSE) values of 17.9–21.5 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and decreased by 4.2–8.3 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform

Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates.

新疆生态质量变化趋势遥感分析

PDF HTML阅读 XML下载 导出引用 引用提醒 新疆生态质量变化趋势遥感分析 DOI: 10.5846/stxb201904280871 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划项目（2017YFB0504203）；中央引导地方发展专项（2017L3012） Remote sensing analysis of ecological quality change in Xinjiang Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:新疆是我国西部生态环境问题比较突出的区域，开展生态状况调查评估、及时掌握生态质量状况及其变化特征是环境保护的一个研究重点。以遥感数据为基础，根据构建垂直干旱指数（Perpendicular Drought Index，PDI）的思想，改变土壤基线所经过的原点，提出表征土壤湿度的垂直湿度指数（Perpendicular Moisture Index，PMI）。基于代表绿度的垂直植被指数（Perpendicular Vegetation Index，PVI）、代表湿度的垂直湿度指数PMI和代表热度的地表温度（Land Surface Temperature，LST），利用空间几何原理创建了植被-湿度-温度生态指数（Vegetation-Moisture-Temperature Ecological Index，VMTEI）。利用MODIS数据计算了新疆2000-2018年的VMTEI，并进行趋势分析。结果表明：VMTEI集成了植被、湿度和热度的信息，能很好地反映新疆生态质量的空间分布和时间变化趋势。新疆生态质量状况总体空间分布格局变化不大，与植被分布特征比较一致，呈现出纬向伸展的变化，不同生态系统VMTEI均值差异明显，湿地生态系统生态质量状态最好，VMTEI均值为0.43左右，其次为森林生态系统；最差的是荒漠生态系统，VMTEI仅为0.28。近20年来，新疆生态状况总体上得到改善，其中，生态状况明显改善的区域占2.332%，主要集中在塔里木河干流、伊犁河、额敏河和天山北坡绿洲经济带，以农田、聚落生态系统为主，主要原因为三北防护林建设、风沙治理、城市绿地面积增加造成的生态质量状况改善；生态状况严重退化的区域占0.430%，主要集中在塔克拉玛干沙漠外围、博斯腾湖、艾比湖以及零星分布于新疆的不同地区，以湿地生态系统为主，主要由于人为因素和气候变化造成平原湖泊以及部分水库的生态退化。 Abstract:Xinjiang is a region with vulnerable eco-environment in western China. It is of significance to carry out eco-environment survey, assessment and timely grasp the ecological status and its changing characteristics for environmental protection. To meet this requirement, this paper developed a Vegetation-Moisture-Temperature Ecological Index (VMTEI) for measuring of region ecology. As an index for assessment of region ecological status, the VMTEI synthesized three important ecological indicators which were frequently used in evaluating ecology. These are greenness, wetness and heat. The three indicators can be represented respectively by three remote sensing indices or components, which are the perpendicular vegetation index (PVI), perpendicular moisture index (PMI), and land surface temperature (LST). Instead of a simple addition or weighted addition of the three indicators, the 3D spatial geometry principle was utilized to compress the three indicators into one to develop the VMTEI for assessing ecological status. The VMTEI of Xinjiang from 2000 to 2018 was calculated and the trend analysis was carried out by Sen+Mann-Kendall method. The results showed that VMTEI synthesized information on vegetation, soil moisture, and heat could well reflect the spatial distribution and temporal change of Xinjiang's ecological quality. The overall spatial distribution pattern of eco-environment in Xinjiang had little change, which is consistent with the distribution of vegetation with the change of latitudinal extension. The mean value of VMTEI in various ecosystems was obviously different. The eco-environment of wetland ecosystem was the best, the mean values of its VMTEI is 0.43, and followed by forest ecosystems. The worst was the desert ecosystem with VMTEI only 0.28. In the past 20 years, Xinjiang's ecological status has been improved overall. The area with obvious improvement of ecological quality status accounted for 2.332%, mainly concentrated in the farmland and settlement ecosystem which distributed in the Tarim River mainstream, Yili River, Emin River, and Tianshan North Slope Oasis Economic Belt. The main reasons of improvement were the construction of the Three-North Shelter Belt, sand management and the increase of urban green space. The area witnessed eco-degradation was 0.430%, mainly located in the wetland ecosystem which distributed in the periphery of the Taklimakan Desert, Bosten Lake, Aibi Lake and scattered in different regions of Xinjiang mainly due to eco-degradation of plain lakes and some reservoirs to human factors and climate change. 参考文献 相似文献 引证文献

The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data.

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

Mapping land use and dynamics of vegetation cover in Southeastern Arabia using remote sensing: the study case of Wilayat Nizwa (Oman) from 1987 to 2016

The region of Wilayat Nizwa, in Northern Oman, is characterized by very high temperature and rare irregular rainfall. Local farmers relay on irrigation to maintain their lands productive. They get water by means of ancestral water acquisition systems called aflaj (plural of falaj). This study focalizes on the physical space of Wilayat Nizwa. Its main goal is to read and analyze the landscape of this area using a method based on multispectral electromagnetic spatial remote sensing. The processing of data from satellites Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI and TIRS relative to January 1987, October 2000, February 2009, and January 2016 allowed us to extract information on the landscape components of Nizwa and its surroundings, to generate a land use map, and to monitor the dynamics of the vegetation cover in Wilayat Niwza from 1987 to 2016. The study zone is mainly a mountainous region, rugged with many gullies and characterized by an important geological diversity and large sedimentary valleys and riverbeds. The non-supervised classification reveals six different classes of which the clay/sandy sedimentary soils and the very clayey rocky/gypsum blocs are the most important ones. The information extracted from the Perpendicular Vegetation Index 1 (PVI1) show that the area of irrigated vegetation remained relatively stable, with a minor increase, which equals to 805.91 ha, in 2009, compared with the spontaneous one in which the rise is 17,588.85 ha. Combined with socioeconomical and climatic parameters, these results would be good indicators to assess the functioning of aflaj and to anticipate the evolution of the local landscape.

Using the red-near infrared spectral to estimate ground cover based on vegetative indices

ABSTRACTRapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.

Using aerial imagery and digital photography to monitor growth and yield in winter wheat

ABSTRACTMonitoring wheat (Triticum aestivum L.) performance throughout the growing season provides information on productivity and yield potential. Remote sensing tools have provided easy and quick measurements without destructive sampling. The objective of this study was to evaluate genetic variability in growth and performance of 20 wheat genotypes under two water regimes (rainfed and irrigated), using spectral vegetation indices (SVI) estimated from aerial imagery and percentage ground cover (%GC) estimated from digital photos. Field experiments were conducted at Bushland, Texas in two growing seasons (2014–2015 and 2015–2016). Digital photographs were taken using a digital camera in each plot, while a manned aircraft collected images of the entire field using a 12-band multiple camera array Tetracam system at three growth stages (tillering, jointing and heading). Results showed that a significant variation exists in SVI, %GC, aboveground biomass and yield among the wheat genotypes mostly at tillering and jointing. Significant relationships for %GC from digital photo at jointing was recorded with Normalized Difference Vegetation Index (NDVI) at tillering (coefficient of determination, R2 = 0.84, p< 0.0001) and with %GC estimated from Perpendicular Vegetation Index (PVI) at tillering (R2 = 0.83, p< 0.0001). Among the indices, Ratio Vegetation Index (RVI), Green-Red VI, Green Leaf Index (GLI), Generalized DVI (squared), DVI, Enhanced VI, Enhanced NDVI, and NDVI explained 37–99% of the variability in aboveground biomass and yield. Results indicate that these indices could be used as an indirect selection tool for screening a large number of early-generation and advanced wheat lines.

Approcher la densité des ligneux par l’indice perpendiculaire de végétation : cas d’une savane arborée au nord de la Tanzanie

Le domaine de Namalok (4 500 ha), situé au nord-est de la Tanzanie, a été protégé par TPC Limited, une société sucrière privée, pour en faire une réserve privée de faune et de flore. La végétation locale est constituée de savanes arbustives à Salvadora persica, de fourrés de Suaeda monoica et de savanes arborées plus au moins denses à Acacia xanthophloea, Faidherbia albida et Acacia tortilis. Afin de répondre à un objectif de gestion suite à la réintroduction de grands herbivores, la composition floristique de ces formations ligneuses a été étudiée en association avec une approche cartographique du degré d’accessibilité des formations ligneuses par les herbivores, exprimé en classes de densité établies selon l’indice perpendiculaire de végétation (IPV). Les résultats révèlent tout d’abord une corrélation très élevée entre la densité calculée des ligneux et l’IPV (r2 = 0,96) et confirment l’intérêt de l’approche proposée d’analyse d’images satellitaires. Dans une perspective plus locale, ils montrent aussi que, sur le plan floristique, les niveaux successifs des densités cartographiées sont composés de 70 espèces végétales et relèvent d’un fond floristique d’espèces pantropicales. Ces savanes montrent également une distribution spécifique relativement équitable et très diverse, avec des fréquences spécifiques voisines les unes des autres. Les savanes arborées représentent 38 % de la surface du domaine. Par ordre croissant de densité, les savanes de moins de 100 arbres/ha couvrent 836 ha, celles comptant 100 à 200 arbres/ha occupent 654 ha et celles ayant des densités de 200 à 300 arbres/ha sont assez peu importantes et n’occupent que 206 ha. Les savanes de densité supérieure à 300 arbres/ha représentent des étendues beaucoup moins vastes, soit seulement 22 ha.

Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot

The present work aims to study the effect of using vegetation indices technique on image segmentation for subdividing an image into the homogeneous regions. Three of these vegetation indices technique has been adopted (i.e. Difference Vegetation-Index (DVI), Perpendicular Vegetation Index (PVI) and Weighted Difference Vegetation Index (WDVI)) for detecting and monitoring vegetation distribution and healthiness. Image binarization method being followed the implementation of the indices to isolating the vegetation areas from the image background. The separated agriculture regions from other land use regions and their percentages are presented for two years (2001 and 2002) of the (ETM+) scenes. The counted areas resulted from 2D-space plot technique and the separated vegetated areas resulted from the using of the vegetation indices are also presented. The separated agriculture regions from the implementation of the DVI-index have proved better than other used indices. Because it showed better coincident approximately with 2D-space plot segmentation.

Application of forest canopy density model for forest cover mapping using LISS-IV satellite data: a case study of Sali watershed, West Bengal

Investigation of forest canopy density has become an important tool for proper management of natural resources. Vegetation cover density can identify the exact forest gaps within a particular area which in turn will provide the appropriate management strategies for future. Forest canopy density has become an essential tool for identifying the exact areas where the afforestation or reforestation programmes needs to be implemented. The aim and objective of this article is to show up the existing density of forest cover using remote sensing and geographic information system tools. Weighted overlay analysis method has been adopted for investigating forest canopy density of Sali river basin, Bankura district, West Bengal. Several indices likewise normalized difference vegetation index, bareness index, shadow index and perpendicular vegetation index etc. have been used for this study. Higher the weight was assigned for greater concentration of vegetation and lower the weight was assigned for lesser concentration of vegetation. Southern part of the region has very high density of forest coverage in comparison with the northern part of the region. It has been observed that 7.48% of the area is at very low density, 12.63% of low density, 24.84% of medium density, 23.92% of high density and 31.13% of very high forest canopy density.